1. SylabUZ
2. Faculty of Exact and Natural Sciences
3. winter term 2023/2024
4. computer science and econometrics - First-cycle studies leading to Bachelor's degree
5. Basics of Machine Learning

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Basics of Machine Learning - course description

Aim of the course

The aim of the course is to familiarize students with the basic machine learning algorithms that are currently widely used in the practical analysis of various types of data sets.

The final goal of the course is for the student to acquire the ability to choose appropriate machine learning methods depending on the practical problem posed. The ability to discover patterns and rules hidden in data. The use of machine learning methods as support in the business decision support process.

Additionally, real data analyzes will be carried out using R software, which is currently very popular among analysts. After this course, the student will be able to use specialized R libraries to solve specific problems using machine learning algorithms.

Prerequisites

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Knowledge of the basics of statistics and probability theory.

Scope

Lecture/Lab:

1. Introduction to machine learning. Basic data mining tasks.
2. Data pre-processing: data cleaning, variable transformations, graphical presentation of variables.
3. Classification of basic machine learning methods. Supervised and unsupervised learning methods. Training and test datasets.
4. Cluster analysis algorithms: hierarchical clustering, K-means method.
5. Assessment of the quality of clustering results.
6. Dimension reduction methods: principal components analysis.
7. Classification algorithms: decision trees, Bayesian network.
8. Regresyjne, statystyczne modele klasyfikacyjne: liniowy i logistyczny.
9. Ocena jakości modeli klasyfikacyjnych: macierz pomyłek, krzywa ROC, trafność klasyfikacji.

Teaching methods

Lecture: traditional and problem-based.

Laboratory: solving research problems using machine learning algorithms using specialized R program libraries. Discussion. Teamwork.

Learning outcomes and methods of theirs verification

Outcome description	Outcome symbols	Methods of verification	The class form

Assignment conditions

Checking the degree of students' preparation and their activity both in the laboratory and during the lecture.

The grade for the laboratory will be based on the results from the colloquium and/or projects.

Recommended reading

1. S. Raschka, V. Mirjalili, Python. Uczenie maszynowe, Helion, 2019.
2. Geron Aurelien: Uczenie maszynowe z użyciem Scikit-Learn i TensorFlow, Helion, 2020.
3. W. Richert, L.P. Coelho, Building Machine Learning Systems with Python, Packt Publishing, 2013.
4. A. C. Muller, S. Guido: Machine learning, Python i data science. Wprowadzenie, Helion, 2021.
5. M. Gągolewski, M. Bartoszuk, A. Cena, Przetwarzanie i analiza danych w języku Python, PWN, 2016.
6. T. Morzy, Eksploracja danych – metody i algorytmy, Wydawnictwo naukowe PWN, Warszawa, 2013.
7. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2007.

Further reading

1. GoodFellow I, Bengio Y., Courville A. Deep learning. Systemy uczące się, PWN, Warszawa, 2018.
2. W. Richert, L.P. Coelho, Building Machine Learning Systems with Python, Packt Publishing, 2013.
3. J. Koronacki, J. Ćwik: Statystyczne systemy uczące się. Wydanie drugie, EXIT, Warszawa, 2007.
4. M. Szeliga, Data science i uczenie maszynowe, Wydawnictwo naukowe PWN, Warszawa, 2017.

Notes

Modified by dr Ewa Synówka (last modification: 10-04-2024 20:23)